Applications of radar measurement technology using 24 GHz, 61 GHz, 80 GHz and 122 GHz FMCW radar sensors

This paper presents a review of radar applications in high-accuracy distance measurement of a target. The radars included in this review are frequency modulated continuous wave (FMCW) radar sensors operating in four different millimeter-wave frequency bands, namely 24 GHz, 61 GHz, 80 GHz and 122 GHz. The radar sensors are used to measure the distance of standard and complex targets in a short range of a few meters, thus indicating that the choice of target and the medium used for radar signal propagation also play a key role in determining the distance measurement accuracy of an FMCW radar. The standard target is a trihedral corner reflector in a laboratory-based free space measurement setup and the complex targets include a piston in an oil-filled hydraulic cylinder and a planar positioning stage used in micromachining. In each of these measurement scenarios, a distance measurement accuracy in micrometer range is achieved due to the use of a sophisticated signal processing algorithm that is based on a combined frequency and phase estimation method. The paper is concluded with a technical comparison of the accuracy achieved by the FMCW radars reviewed in this article with other related works.

Object detection for automotive radar point clouds – a comparison

Automotive radar perception is an integral part of automated driving systems. Radar sensors benefit from their excellent robustness against adverse weather conditions such as snow, fog, or heavy rain. Despite the fact that machine-learning-based object detection is traditionally a camera-based domain, vast progress has been made for lidar sensors, and radar is also catching up. Recently, several new techniques for using machine learning algorithms towards the correct detection and classification of moving road users in automotive radar data have been introduced. However, most of them have not been compared to other methods or require next generation radar sensors which are far more advanced than current conventional automotive sensors. This article makes a thorough comparison of existing and novel radar object detection algorithms with some of the most successful candidates from the image and lidar domain. All experiments are conducted using a conventional automotive radar system. In addition to introducing all architectures, special attention is paid to the necessary point cloud preprocessing for all methods. By assessing all methods on a large and open real world data set, this evaluation provides the first representative algorithm comparison in this domain and outlines future research directions.

Emergency Rapid Response to Epileptic Seizures - A Novel IOT Framework for Smart Cities

Epilepsy is a common neurological disorder that results in seizures in patients of all ages. The frequency of seizure episodes can be controlled by prescribing anti-seizure drugs. Drug-resistant epilepsy is a condition where the seizures are uncontrolled by strong medications. Such patients are at a high risk of getting seizures frequently and prone to injuries due to sudden falls. Many countries prohibit epileptic patients from driving as sudden seizure attacks can cause loss of lives and property. In the past decades immense work has been carried out in the to monitor the seizure activity in patients and alert caregivers to extend help in emergencies. The study proposes a smart health care Internet of things framework to provide immediate help to the epileptic patient during an episode while travelling in a self-driving car. In the proposed framework, the seizure alert from a wearable device of the patient is transmitted to the control application via cloud. The control application also receives data from the vision, ultrasonic, and radar sensors. The critical information of seizure alert and the sensor data commands the car to force stop. The seizure google map location of the car is sent to the patient’s caregiver as well as the registered hospital. Many applications are being developed to provide luxury and comfort to fully automated car drivers. Far from providing luxury to the driver Emergency Rapid Response to Epileptic Seizure aims to propose a solution that could save the life of an epileptic patient who is drug-resistant or prone to frequent attacks despite severe medications. The experimental results on synchonization of clouds show that the minimum time is 30 sec 30 ms and maximum time is 31 sec 63 ms. The experimental results prove that its recommended to alert the patient's caregiver directly from control application rather than alerting via cloud.

The normalised Sentinel-1 Global Backscatter Model, mapping Earth’s land surface with C-band microwaves

We present a new perspective on Earth’s land surface, providing a normalised microwave backscatter map from spaceborne Synthetic Aperture Radar (SAR) observations. The Sentinel-1 Global Backscatter Model (S1GBM) describes Earth for the period 2016–17 by the mean C-band radar cross section in VV- and VH-polarisation at a 10 m sampling. We processed 0.5 million Sentinel-1 scenes totalling 1.1 PB and performed semi-automatic quality curation and backscatter harmonisation related to orbit geometry effects. The overall mosaic quality excels (the few) existing datasets, with minimised imprinting from orbit discontinuities and successful angle normalisation in large parts of the world. Regions covered by only one or two Sentinel-1 orbits remain challenging, owing to insufficient angular variation and not yet perfect sub-swath thermal noise correction. Supporting the design and verification of upcoming radar sensors, the obtained S1GBM data potentially also serve land cover classification and determination of vegetation and soil states. Here, we demonstrate, as an example of its potential use, the mapping of permanent water bodies and evaluate against the Global Surface Water benchmark.

On the Development of Autonomous Vehicle Safety Distance by an RSS Model Based on a Variable Focus Function Camera

Today, a lot of research on autonomous driving technology is being conducted, and various vehicles with autonomous driving functions, such as ACC (adaptive cruise control) are being released. The autonomous vehicle recognizes obstacles ahead by the fusion of data from various sensors, such as lidar and radar sensors, including camera sensors. As the number of vehicles equipped with such autonomous driving functions increases, securing safety and reliability is a big issue. Recently, Mobileye proposed the RSS (responsibility-sensitive safety) model, which is a white box mathematical model, to secure the safety of autonomous vehicles and clarify responsibility in the case of an accident. In this paper, a method of applying the RSS model to a variable focus function camera that can cover the recognition range of a lidar sensor and a radar sensor with a single camera sensor is considered. The variables of the RSS model suitable for the variable focus function camera were defined, the variable values were determined, and the safe distances for each velocity were derived by applying the determined variable values. In addition, as a result of considering the time required to obtain the data, and the time required to change the focal length of the camera, it was confirmed that the response time obtained using the derived safe distance was a valid result.

A New Current-Shaping Technique Based on a Feedback Injection Mechanism to Reduce VCO Phase Noise

Inductor-capacitor voltage controlled oscillators (LC-VCOs) are the most common type of oscillator used in sensors systems, such as transceivers for wireless sensor networks (WSNs), VCO-based reading circuits, VCO-based radar sensors, etc. This work presents a technique to reduce the LC-VCOs phase noise using a new current-shaping method based on a feedback injection mechanism with only two additional transistors. This technique consists of keeping the negative resistance seen from LC tank constant throughout the oscillation cycle, achieving a significant phase noise reduction with a very low area increase. To test this method an LC-VCO was designed, fabricated and measured on a wafer using 90 nm CMOS technology with 1.2 V supply voltage. The oscillator outputs were buffered using source followers to provide additional isolation from load variations and to boost the output power. The tank was tuned to 1.8 GHz, comprising two 1.15 nH with 1.5 turns inductors with a quality factor (Q) of 14, a 3.27 pF metal-oxide-metal capacitor, and two varactors. The measured phase noise was −112 dBc/Hz at 1 MHz offset. Including the pads, the chip area is 750 × 850 μm2.